






import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)




def 				A		(					lowercase__  :  Optional[int] ,		lowercase__  :  Tuple  )		-> Optional[int]:
	UpperCamelCase__ :List[Any]						=      XCLIPTextConfig()

	# derive patch size from model name
	UpperCamelCase__ :Dict						=      model_name.find("""patch"""  )
	UpperCamelCase__ :Any						=      int(model_name[start_idx + len("""patch"""  ) : start_idx + len("""patch"""  ) + 2]  )
	UpperCamelCase__ :Dict						=      XCLIPVisionConfig(patch_size=lowercase__ ,		num_frames=lowercase__  )

	if "large" in model_name:
		UpperCamelCase__ :Dict						=      768
		UpperCamelCase__ :List[str]						=      3072
		UpperCamelCase__ :int						=      12

		UpperCamelCase__ :Tuple						=      1024
		UpperCamelCase__ :Union[str, Any]						=      4096
		UpperCamelCase__ :List[str]						=      16
		UpperCamelCase__ :Dict						=      24
		UpperCamelCase__ :Any						=      768
		UpperCamelCase__ :List[Any]						=      3072

	if model_name == "xclip-large-patch14-16-frames":
		UpperCamelCase__ :Optional[Any]						=      336

	UpperCamelCase__ :List[str]						=      XCLIPConfig.from_text_vision_configs(lowercase__ ,		lowercase__  )

	if "large" in model_name:
		UpperCamelCase__ :List[str]						=      768

	return config




def 				A		(					lowercase__  :  Dict  )		-> Optional[int]:
	# text encoder
	if name == "token_embedding.weight":
		UpperCamelCase__ :str						=      name.replace("""token_embedding.weight""" ,		"""text_model.embeddings.token_embedding.weight"""  )
	if name == "positional_embedding":
		UpperCamelCase__ :str						=      name.replace("""positional_embedding""" ,		"""text_model.embeddings.position_embedding.weight"""  )
	if "ln_1" in name:
		UpperCamelCase__ :List[Any]						=      name.replace("""ln_1""" ,		"""layer_norm1"""  )
	if "ln_2" in name:
		UpperCamelCase__ :Tuple						=      name.replace("""ln_2""" ,		"""layer_norm2"""  )
	if "c_fc" in name:
		UpperCamelCase__ :Optional[int]						=      name.replace("""c_fc""" ,		"""fc1"""  )
	if "c_proj" in name:
		UpperCamelCase__ :Optional[Any]						=      name.replace("""c_proj""" ,		"""fc2"""  )
	if name.startswith("""transformer.resblocks"""  ):
		UpperCamelCase__ :Union[str, Any]						=      name.replace("""transformer.resblocks""" ,		"""text_model.encoder.layers"""  )
	if "attn.out_proj" in name and "message" not in name:
		UpperCamelCase__ :int						=      name.replace("""attn.out_proj""" ,		"""self_attn.out_proj"""  )
	if "ln_final" in name:
		UpperCamelCase__ :Optional[Any]						=      name.replace("""ln_final""" ,		"""text_model.final_layer_norm"""  )
	# visual encoder
	if name == "visual.class_embedding":
		UpperCamelCase__ :Union[str, Any]						=      name.replace("""visual.class_embedding""" ,		"""vision_model.embeddings.class_embedding"""  )
	if name == "visual.positional_embedding":
		UpperCamelCase__ :Any						=      name.replace("""visual.positional_embedding""" ,		"""vision_model.embeddings.position_embedding.weight"""  )
	if name.startswith("""visual.transformer.resblocks"""  ):
		UpperCamelCase__ :Optional[int]						=      name.replace("""visual.transformer.resblocks""" ,		"""vision_model.encoder.layers"""  )
	if "visual.conv1" in name:
		UpperCamelCase__ :List[str]						=      name.replace("""visual.conv1""" ,		"""vision_model.embeddings.patch_embedding"""  )
	if "visual.ln_pre" in name:
		UpperCamelCase__ :Optional[int]						=      name.replace("""visual.ln_pre""" ,		"""vision_model.pre_layernorm"""  )
	if "visual.ln_post" in name:
		UpperCamelCase__ :Any						=      name.replace("""visual.ln_post""" ,		"""vision_model.post_layernorm"""  )
	if "visual.proj" in name:
		UpperCamelCase__ :Union[str, Any]						=      name.replace("""visual.proj""" ,		"""visual_projection.weight"""  )
	if "text_projection" in name:
		UpperCamelCase__ :List[Any]						=      name.replace("""text_projection""" ,		"""text_projection.weight"""  )
	# things on top
	if "prompts_visual_proj" in name:
		UpperCamelCase__ :List[Any]						=      name.replace("""prompts_visual_proj""" ,		"""prompts_visual_projection"""  )
	if "prompts_visual_ln" in name:
		UpperCamelCase__ :Union[str, Any]						=      name.replace("""prompts_visual_ln""" ,		"""prompts_visual_layernorm"""  )
	# mit
	if name == "mit.positional_embedding":
		UpperCamelCase__ :List[str]						=      name.replace("""positional""" ,		"""position"""  )
	if name.startswith("""mit.resblocks"""  ):
		UpperCamelCase__ :Optional[Any]						=      name.replace("""mit.resblocks""" ,		"""mit.encoder.layers"""  )
	# prompts generator
	if name.startswith("""prompts_generator.norm"""  ):
		UpperCamelCase__ :Optional[Any]						=      name.replace("""prompts_generator.norm""" ,		"""prompts_generator.layernorm"""  )

	return name




def 				A		(					lowercase__  :  Optional[int] ,		lowercase__  :  Dict  )		-> Dict:
	for key in orig_state_dict.copy().keys():
		UpperCamelCase__ :int						=      orig_state_dict.pop(lowercase__  )

		if "attn.in_proj" in key:
			UpperCamelCase__ :Any						=      key.split("""."""  )
			if key.startswith("""visual"""  ):
				UpperCamelCase__ :str						=      key_split[3]
				UpperCamelCase__ :str						=      config.vision_config.hidden_size
				if "message_attn" in key:
					if "weight" in key:
						UpperCamelCase__ :List[str]						=      val[
						    :dim, :
						]
						UpperCamelCase__ :Any						=      val[
						    dim : dim * 2, :
						]
						UpperCamelCase__ :Dict						=      val[
						    -dim:, :
						]
					else:
						UpperCamelCase__ :Optional[Any]						=      val[
						    :dim
						]
						UpperCamelCase__ :str						=      val[
						    dim : dim * 2
						]
						UpperCamelCase__ :List[Any]						=      val[
						    -dim:
						]
				else:
					if "weight" in key:
						UpperCamelCase__ :List[Any]						=      val[
						    :dim, :
						]
						UpperCamelCase__ :Optional[Any]						=      val[
						    dim : dim * 2, :
						]
						UpperCamelCase__ :Union[str, Any]						=      val[
						    -dim:, :
						]
					else:
						UpperCamelCase__ :int						=      val[:dim]
						UpperCamelCase__ :List[Any]						=      val[
						    dim : dim * 2
						]
						UpperCamelCase__ :Any						=      val[-dim:]
			elif key.startswith("""mit"""  ):
				UpperCamelCase__ :Dict						=      key_split[2]
				UpperCamelCase__ :List[Any]						=      config.vision_config.mit_hidden_size
				if "weight" in key:
					UpperCamelCase__ :Dict						=      val[:dim, :]
					UpperCamelCase__ :Any						=      val[dim : dim * 2, :]
					UpperCamelCase__ :List[str]						=      val[-dim:, :]
				else:
					UpperCamelCase__ :Union[str, Any]						=      val[:dim]
					UpperCamelCase__ :List[str]						=      val[dim : dim * 2]
					UpperCamelCase__ :str						=      val[-dim:]
			else:
				UpperCamelCase__ :Dict						=      key_split[2]
				UpperCamelCase__ :Tuple						=      config.text_config.hidden_size
				if "weight" in key:
					UpperCamelCase__ :List[str]						=      val[:dim, :]
					UpperCamelCase__ :Optional[Any]						=      val[
					    dim : dim * 2, :
					]
					UpperCamelCase__ :Dict						=      val[-dim:, :]
				else:
					UpperCamelCase__ :Tuple						=      val[:dim]
					UpperCamelCase__ :Tuple						=      val[
					    dim : dim * 2
					]
					UpperCamelCase__ :Dict						=      val[-dim:]
		else:
			UpperCamelCase__ :Dict						=      rename_key(lowercase__  )
			if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
				UpperCamelCase__ :Union[str, Any]						=      val.T
			UpperCamelCase__ :int						=      val

	return orig_state_dict




def 				A		(					lowercase__  :  Dict  )		-> Union[str, Any]:
	if num_frames == 8:
		UpperCamelCase__ :int						=      """eating_spaghetti_8_frames.npy"""
	elif num_frames == 16:
		UpperCamelCase__ :int						=      """eating_spaghetti.npy"""
	elif num_frames == 32:
		UpperCamelCase__ :Any						=      """eating_spaghetti_32_frames.npy"""
	UpperCamelCase__ :List[Any]						=      hf_hub_download(
	    repo_id="""hf-internal-testing/spaghetti-video""" ,		filename=lowercase__ ,		repo_type="""dataset""" ,		)
	UpperCamelCase__ :Optional[Any]						=      np.load(lowercase__  )
	return list(lowercase__  )




def 				A		(					lowercase__  :  int ,		lowercase__  :  Dict=None ,		lowercase__  :  List[str]=False  )		-> int:
	UpperCamelCase__ :Dict						=      {
	    # fully supervised kinetics-400 checkpoints
	    """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
	    """xclip-base-patch32-16-frames""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
	    ),
	    """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
	    """xclip-base-patch16-16-frames""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
	    ),
	    """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
	    """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
	    # fully supervised kinetics-600 checkpoints
	    """xclip-base-patch16-kinetics-600""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
	    ),
	    """xclip-base-patch16-kinetics-600-16-frames""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
	    ),
	    """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
	    # few shot
	    """xclip-base-patch16-hmdb-2-shot""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
	    ),
	    """xclip-base-patch16-hmdb-4-shot""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
	    ),
	    """xclip-base-patch16-hmdb-8-shot""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
	    ),
	    """xclip-base-patch16-hmdb-16-shot""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
	    ),
	    """xclip-base-patch16-ucf-2-shot""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
	    ),
	    """xclip-base-patch16-ucf-4-shot""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
	    ),
	    """xclip-base-patch16-ucf-8-shot""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
	    ),
	    """xclip-base-patch16-ucf-16-shot""": (
	        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
	    ),
	    # zero shot
	    """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
	}

	UpperCamelCase__ :List[Any]						=      model_to_url[model_name]
	UpperCamelCase__ :Optional[int]						=      8
	if "16-frames" in model_name:
		UpperCamelCase__ :Union[str, Any]						=      16
	elif "shot" in model_name:
		UpperCamelCase__ :Any						=      32

	UpperCamelCase__ :Dict						=      get_xclip_config(lowercase__ ,		lowercase__  )
	UpperCamelCase__ :List[Any]						=      XCLIPModel(lowercase__  )
	model.eval()

	if "drive" in checkpoint_url:
		UpperCamelCase__ :int						=      """pytorch_model.bin"""
		gdown.cached_download(lowercase__ ,		lowercase__ ,		quiet=lowercase__  )
		UpperCamelCase__ :str						=      torch.load(lowercase__ ,		map_location="""cpu"""  )["""model"""]
	else:
		UpperCamelCase__ :Tuple						=      torch.hub.load_state_dict_from_url(lowercase__  )["""model"""]

	UpperCamelCase__ :Optional[int]						=      convert_state_dict(lowercase__ ,		lowercase__  )

	UpperCamelCase__ :Tuple						=      XCLIPModel(lowercase__  )
	UpperCamelCase__				,		UpperCamelCase__ :str						=      model.load_state_dict(lowercase__ ,		strict=lowercase__  )
	assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
	model.eval()

	UpperCamelCase__ :Dict						=      336 if model_name == """xclip-large-patch14-16-frames""" else 224
	UpperCamelCase__ :Union[str, Any]						=      VideoMAEImageProcessor(size=lowercase__  )
	UpperCamelCase__ :Any						=      CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""  )
	UpperCamelCase__ :Any						=      CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""  )
	UpperCamelCase__ :Optional[Any]						=      XCLIPProcessor(image_processor=lowercase__ ,		tokenizer=lowercase__  )

	UpperCamelCase__ :Union[str, Any]						=      prepare_video(lowercase__  )
	UpperCamelCase__ :List[Any]						=      processor(
	    text=["""playing sports""", """eating spaghetti""", """go shopping"""] ,		videos=lowercase__ ,		return_tensors="""pt""" ,		padding=lowercase__  )

	print("""Shape of pixel values:""" ,		inputs.pixel_values.shape  )

	with torch.no_grad():
		UpperCamelCase__ :str						=      model(**lowercase__  )

	# Verify outputs
	UpperCamelCase__ :Dict						=      outputs.logits_per_video
	UpperCamelCase__ :Dict						=      logits_per_video.softmax(dim=1  )
	print("""Probs:""" ,		lowercase__  )
	# kinetics-400
	if model_name == "xclip-base-patch32":
		UpperCamelCase__ :Optional[Any]						=      torch.tensor([[0.0019, 0.9951, 0.0030]]  )
	elif model_name == "xclip-base-patch32-16-frames":
		UpperCamelCase__ :Optional[int]						=      torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]]  )
	elif model_name == "xclip-base-patch16":
		UpperCamelCase__ :Dict						=      torch.tensor([[0.0083, 0.9681, 0.0236]]  )
	elif model_name == "xclip-base-patch16-16-frames":
		UpperCamelCase__ :str						=      torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]]  )
	elif model_name == "xclip-large-patch14":
		UpperCamelCase__ :Dict						=      torch.tensor([[0.0062, 0.9864, 0.0075]]  )
	elif model_name == "xclip-large-patch14-16-frames":
		UpperCamelCase__ :str						=      torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]]  )
	# kinetics-600
	elif model_name == "xclip-base-patch16-kinetics-600":
		UpperCamelCase__ :List[str]						=      torch.tensor([[0.0555, 0.8914, 0.0531]]  )
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		UpperCamelCase__ :int						=      torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]]  )
	elif model_name == "xclip-large-patch14-kinetics-600":
		UpperCamelCase__ :str						=      torch.tensor([[0.0036, 0.9920, 0.0045]]  )
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		UpperCamelCase__ :Any						=      torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-4-shot":
		UpperCamelCase__ :List[Any]						=      torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		UpperCamelCase__ :Dict						=      torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		UpperCamelCase__ :Any						=      torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]]  )
	elif model_name == "xclip-base-patch16-ucf-2-shot":
		UpperCamelCase__ :Optional[int]						=      torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]  )
	elif model_name == "xclip-base-patch16-ucf-4-shot":
		UpperCamelCase__ :List[str]						=      torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]  )
	elif model_name == "xclip-base-patch16-ucf-8-shot":
		UpperCamelCase__ :Dict						=      torch.tensor([[0.0027, 0.9904, 0.0070]]  )
	elif model_name == "xclip-base-patch16-ucf-16-shot":
		UpperCamelCase__ :int						=      torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]]  )
	# zero shot
	elif model_name == "xclip-base-patch16-zero-shot":
		UpperCamelCase__ :Optional[Any]						=      torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-03]]  )
	else:
		raise ValueError(f"""Model name {model_name} not supported"""  )
	assert torch.allclose(lowercase__ ,		lowercase__ ,		atol=1E-3  )
	print("""Looks ok!"""  )

	if pytorch_dump_folder_path is not None:
		print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
		model.save_pretrained(lowercase__  )

	if push_to_hub:
		print("""Pushing model, processor and slow tokenizer files to the hub..."""  )
		model.push_to_hub(lowercase__ ,		organization="""nielsr"""  )
		processor.push_to_hub(lowercase__ ,		organization="""nielsr"""  )
		slow_tokenizer.push_to_hub(lowercase__ ,		organization="""nielsr"""  )


if __name__ == "__main__":
							UpperCamelCase        =				argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="xclip-base-patch32",
							    type=str,
							    help="Name of the model.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
							)

							UpperCamelCase        =				parser.parse_args()
							convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFTransfoXLForSequenceClassification,
     TFTransfoXLLMHeadModel,
     TFTransfoXLModel,
 )

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   ):
         snake_case											=							parent
         snake_case											=							1_3
         snake_case											=							7
         snake_case											=							3_0
         snake_case											=							self.seq_length + self.mem_len
         snake_case											=							1_5
         snake_case											=							True
         snake_case											=							True
         snake_case											=							9_9
         snake_case											=							[1_0, 5_0, 8_0]
         snake_case											=							3_2
         snake_case											=							3_2
         snake_case											=							4
         snake_case											=							8
         snake_case											=							1_2_8
         snake_case											=							2
         snake_case											=							2
         snake_case											=							None
         snake_case											=							1
         snake_case											=							0
         snake_case											=							3
         snake_case											=							self.vocab_size - 1
         snake_case											=							0.01
    def 						a_     (			self       ):
         snake_case											=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size       )
         snake_case											=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size       )

         snake_case											=							None
         if self.use_labels:
              snake_case											=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size       )

         snake_case											=							TransfoXLConfig(
             vocab_size=self.vocab_size     ,   mem_len=self.mem_len     ,   clamp_len=self.clamp_len     ,   cutoffs=self.cutoffs     ,   d_model=self.hidden_size     ,   d_embed=self.d_embed     ,   n_head=self.num_attention_heads     ,   d_head=self.d_head     ,   d_inner=self.d_inner     ,   div_val=self.div_val     ,   n_layer=self.num_hidden_layers     ,   eos_token_id=self.eos_token_id     ,   pad_token_id=self.vocab_size - 1     ,   init_range=self.init_range     ,   num_labels=self.num_labels     ,   )

         return (config, input_ids_a, input_ids_a, lm_labels)
    def 						a_     (			self       ):
         random.seed(self.seed       )
         tf.random.set_seed(self.seed       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFTransfoXLModel(__snake_case       )

         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         snake_case											=							{'''input_ids''': input_ids_a, '''mems''': mems_a}

         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         self.parent.assertEqual(hidden_states_a.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(hidden_states_a.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]     ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,   )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]     ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFTransfoXLLMHeadModel(__snake_case       )

         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         snake_case											=							{'''input_ids''': input_ids_a, '''labels''': lm_labels}
         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         snake_case					,    snake_case											=							model([input_ids_a, mems_a]       ).to_tuple()

         snake_case											=							{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         self.parent.assertEqual(lm_logits_a.shape     ,   (self.batch_size, self.seq_length, self.vocab_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]     ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,   )

         self.parent.assertEqual(lm_logits_a.shape     ,   (self.batch_size, self.seq_length, self.vocab_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]     ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFTransfoXLForSequenceClassification(__snake_case       )
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)       )




    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         ((snake_case)					,    (snake_case)					,    (snake_case)					,    (snake_case))											=							config_and_inputs
         snake_case											=							{'''input_ids''': input_ids_a}
         return config, inputs_dict






@require_tf
class     A__   (       snake_case__			,   snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    __magic_name__         =						() if is_tf_available() else ()
    __magic_name__         =						(
        {
            'feature-extraction': TFTransfoXLModel,
            'text-classification': TFTransfoXLForSequenceClassification,
            'text-generation': TFTransfoXLLMHeadModel,
            'zero-shot': TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
              # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
              # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
              # tokenizer.
              return True

         return False
    def 						a_     (			self       ):
         snake_case											=							TFTransfoXLModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   d_embed=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    def 						a_     (			self       ):
         self.model_tester.set_seed()
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*__snake_case       )
    def 						a_     (			self       ):
         self.model_tester.set_seed()
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()
         snake_case											=							[TFTransfoXLForSequenceClassification]

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              assert isinstance(model.get_input_embeddings()     ,   tf.keras.layers.Layer       )
              if model_class in list_other_models_with_output_ebd:
                   snake_case											=							model.get_output_embeddings()
                   assert isinstance(__snake_case     ,   tf.keras.layers.Layer       )
                   snake_case											=							model.get_bias()
                   assert name is None
              else:
                   snake_case											=							model.get_output_embeddings()
                   assert x is None
                   snake_case											=							model.get_bias()
                   assert name is None
    def 						a_     (			self       ):
         # TODO JP: Make TransfoXL XLA compliant
         pass
    @slow
    def 						a_     (			self       ):
         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case											=							TFTransfoXLModel.from_pretrained(__snake_case       )
              self.assertIsNotNone(__snake_case       )




    @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''       )
    def 						a_     (			self       ):
         pass






@require_tf
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    @unittest.skip('''Skip test until #12651 is resolved.'''       )
    @slow
    def 						a_     (			self       ):
         snake_case											=							TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''       )
         # fmt: off
         snake_case											=							tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]     ,   dtype=tf.intaa       )  # noqa: E231
         # fmt: on
         #  In 1991 , the remains of Russian Tsar Nicholas II and his family
         #  ( except for Alexei and Maria ) are discovered .
         #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
         #  remainder of the story . 1883 Western Siberia ,
         #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
         #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
         #  father initially slaps him for making such an accusation , Rasputin watches as the
         #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
         #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
         #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

         # fmt: off
         snake_case											=							[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
         # fmt: on
         #  In 1991, the remains of Russian Tsar Nicholas II and his family (
         #  except for Alexei and Maria ) are discovered. The voice of young son,
         #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
         #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
         #  and a group of men to perform magic. Rasputin has a vision and
         #  denounces one of the men as a horse thief. Although his father initially
         #  slaps him for making such an accusation, Rasputin watches as the man
         #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
         #  of the Virgin Mary, prompting him to become a priest.
         #  Rasputin quickly becomes famous, with people, even a bishop, begging for
         #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
         # Nicholas II and his family were discovered. The voice of <unk> young son,
         # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

         snake_case											=							model.generate(__snake_case     ,   max_length=2_0_0     ,   do_sample=__snake_case       )
         self.assertListEqual(output_ids[0].numpy().tolist()     ,   __snake_case       )




